9786139987955 - gaussian mixture model: application to medical image classification de kumar, a. vignesh; harikumar, r. (7 resultados)
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Taschenbuch. Condición: Neu. Gaussian Mixture Model | Application to Medical Image Classification | A. Vignesh Kumar (u. a.) | Taschenbuch | 56 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139987955 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]pr…eigu[dot]de | Anbieter: preigu.
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Condición: Hervorragend. Zustand: Hervorragend | Seiten: 56 | Sprache: Englisch | Produktart: Bücher | Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used…for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for t…he parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques. 56 pp. Englisch.
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar A. VigneshA. Vignesh Kumar, Completed M.E(CSE) & doing Ph.D from Anna University,Chennai and having 5 Years of Academic Experience.Gaussian Mixture Model (GMM) is the probabilistic model, it works… well with the classificati.
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the p…arameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the pa…rameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques.
